Building a Production AI Chatbot with RAG, LangChain & LangGraph (Live Coding)

Building an end-to-end, multi-tenant AI Chatbot SaaS platform from scratch. In this live coding series, we are building far more than a basic demo. We will design a full-stack platform where users can sign up, manage their own knowledge bases with complete tenant isolation, generate API keys, and embed a custom AI chatbot widget into any website using our React web component. Architecture & Core Highlights: Backend & Vector Storage: Python, LangChain, and LangGraph powered by PostgreSQL and pgvector for robust, multi-tenant vector isolation. Agentic Workflows: Orchestrating query analysis, document retrieval, state persistence, and fallbacks with LangGraph. Multi-Tenancy & Auth: Implementing isolated vector spaces/schemas per tenant, custom API key generation, and rate-limiting. Admin Dashboard: Built with React and Vite to manage document uploads, view usage metrics, manage keys, and customize bot parameters. Embeddable Web Component: A lightweight, customizable React chat widget designed for external site integration with tenant key authentication. Full Tech Stack: Backend: Python, FastAPI, LangChain, LangGraph Database & Vectors: PostgreSQL with pgvector Frontend: React, Vite, Tailwind CSS Embed: Custom React Web Component / Embed Script Resources & Links: GitHub Repository: https://github.com/codernex/nexbot Drop your questions, architecture suggestions, or debugging thoughts directly in the live chat. Subscribe and turn on notifications so you catch every stream in the series. #LangChain #LangGraph #pgvector #PostgreSQL #ReactJS #FullStack #RAG #SaaS #Python #LiveCoding